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Dataset statistics

Number of variables

16

Number of observations

1928

Missing cells

1254

Missing cells (%)

4.1%

Duplicate rows

0

Duplicate rows (%)

0.0%

Total size in memory

241.1 KiB

Average record size in memory

128.1 B

Variable types

Numeric

4

Categorical

12

Alerts

Reproduction

id
Real number (ℝ)

UNIQUE 

Distinct

1928

Distinct (%)

100.0%

Missing

0

Missing (%)

0.0%

Infinite

0

Infinite (%)

0.0%

Mean

1264.1598

 

Minimum

3

Maximum

2544

Zeros

0

Zeros (%)

0.0%

Negative

0

Negative (%)

0.0%

Memory size

15.2 KiB

2023-12-30T02:23:58.663337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

More details

(bins=50)

pathrise_status
Categorical

HIGH CORRELATION 

Distinct

6

Distinct (%)

0.3%

Missing

0

Missing (%)

0.0%

Memory size

15.2 KiB

 

Placed

956 

Withdrawn

398 

Withdrawn (Trial)

276 

Closed Lost

182 

Withdrawn (Failed)

 

82

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

primary_track
Categorical

Distinct

6

Distinct (%)

0.3%

Missing

0

Missing (%)

0.0%

Memory size

15.2 KiB

 

SWE

1306 

PSO

233 

Design

205 

Data

178 

Web

 

4

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

cohort_tag
Categorical

Distinct

47

Distinct (%)

2.4%

Missing

6

Missing (%)

0.3%

Memory size

15.2 KiB

 

JAN19A

 

113

DEC18A

 

102

JAN20A

 

82

OCT18B

 

72

OCT18A

 

70

Other values (42)

1483 

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

program_duration_days
Real number (ℝ)

ZEROS 

Distinct

411

Distinct (%)

21.3%

Missing

0

Missing (%)

0.0%

Infinite

0

Infinite (%)

0.0%

Mean

136.09855

 

Minimum

0

Maximum

548

Zeros

217

Zeros (%)

11.3%

Negative

0

Negative (%)

0.0%

Memory size

15.2 KiB

2023-12-30T02:24:00.889508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

More details

(bins=50)

placed
Categorical

HIGH CORRELATION 

Distinct

2

Distinct (%)

0.1%

Missing

0

Missing (%)

0.0%

Memory size

15.2 KiB

 

0

972 

1

956 

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

employment_status
Categorical

MISSING 

Distinct

5

Distinct (%)

0.3%

Missing

182

Missing (%)

9.4%

Memory size

15.2 KiB

 

Student

579 

Unemployed

504 

Employed Full-Time

329 

Employed Part-Time

199 

Contractor

135 

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

highest_level_of_education
Categorical

MISSING 

Distinct

7

Distinct (%)

0.4%

Missing

42

Missing (%)

2.2%

Memory size

15.2 KiB

 

Bachelor's Degree

1036 

Master's Degree

605 

Doctorate or Professional Degree

104 

Some College, No Degree

 

102

GED or equivalent

 

14

Other values (2)

 

25

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

length_of_job_search
Categorical

MISSING 

Distinct

5

Distinct (%)

0.3%

Missing

66

Missing (%)

3.4%

Memory size

15.2 KiB

 

Less than one month

621 

1-2 months

610 

3-5 months

372 

6 months to a year

171 

Over a year

88 

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

biggest_challenge_in_search
Categorical

MISSING 

Distinct

10

Distinct (%)

0.5%

Missing

20

Missing (%)

1.0%

Memory size

15.2 KiB

 

Hearing back on my applications

737 

Getting past final round interviews

234 

Technical interviewing

224 

Lack of relevant experience

175 

Getting past mid-stage interviews

160 

Other values (5)

378 

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

professional_experience
Categorical

MISSING 

Distinct

4

Distinct (%)

0.2%

Missing

161

Missing (%)

8.4%

Memory size

15.2 KiB

 

1-2 years

640 

Less than one year

478 

3-4 years

444 

5+ years

205 

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

work_authorization_status
Categorical

MISSING 

Distinct

9

Distinct (%)

0.5%

Missing

221

Missing (%)

11.5%

Memory size

15.2 KiB

 

Citizen

831 

F1 Visa/OPT

488 

Green Card

142 

F1 Visa/CPT

97 

Other

 

82

Other values (4)

 

67

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

number_of_interviews
Real number (ℝ)

MISSING  ZEROS 

Distinct

20

Distinct (%)

1.1%

Missing

172

Missing (%)

8.9%

Infinite

0

Infinite (%)

0.0%

Mean

2.2038724

 

Minimum

0

Maximum

20

Zeros

579

Zeros (%)

30.0%

Negative

0

Negative (%)

0.0%

Memory size

15.2 KiB

2023-12-30T02:24:06.552225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

More details

(bins=20)

number_of_applications
Real number (ℝ)

ZEROS 

Distinct

40

Distinct (%)

2.1%

Missing

0

Missing (%)

0.0%

Infinite

0

Infinite (%)

0.0%

Mean

36.685166

 

Minimum

0

Maximum

1000

Zeros

55

Zeros (%)

2.9%

Negative

0

Negative (%)

0.0%

Memory size

15.2 KiB

2023-12-30T02:24:07.430421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

More details

(bins=40)

gender
Categorical

IMBALANCE  MISSING 

Distinct

4

Distinct (%)

0.3%

Missing

371

Missing (%)

19.2%

Memory size

15.2 KiB

 

Male

1134 

Female

410 

Decline to Self Identify

 

10

Non-Binary

 

3

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

race
Categorical

Distinct

9

Distinct (%)

0.5%

Missing

13

Missing (%)

0.7%

Memory size

15.2 KiB

 

East Asian or Asian American

703 

South Asian or Indian American

420 

Non-Hispanic White or Euro-American

414 

Latino or Hispanic American

119 

Black, Afro-Caribbean, or African American

91 

Other values (4)

168 

More details

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Common Values

Length

Histogram of lengths of the category

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

2023-12-30T02:23:55.470990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

2023-12-30T02:24:10.341779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

2023-12-30T02:23:57.007932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

 

id

pathrise_status

primary_track

cohort_tag

program_duration_days

placed

employment_status

highest_level_of_education

length_of_job_search

biggest_challenge_in_search

professional_experience

work_authorization_status

number_of_interviews

number_of_applications

gender

race

0

3

Closed Lost

Design

AUG19B

0.0

0

Employed Part-Time

Master's Degree

Less than one month

Figuring out which jobs to apply for

Less than one year

Citizen

0.0

0

Male

East Asian or Asian American

1

4

Closed Lost

PSO

AUG19B

0.0

0

Contractor

Bachelor's Degree

Less than one month

Getting past final round interviews

Less than one year

Citizen

5.0

25

Male

Decline to Self Identify

2

5

Placed

SWE

AUG19A

89.0

1

Unemployed

Bachelor's Degree

1-2 months

Hearing back on my applications

1-2 years

F1 Visa/OPT

10.0

100

Male

East Asian or Asian American

3

6

Closed Lost

SWE

AUG19A

0.0

0

Employed Full-Time

Master's Degree

1-2 months

Technical interviewing

3-4 years

Green Card

5.0

100

Male

East Asian or Asian American

4

7

Closed Lost

SWE

AUG19B

0.0

0

Employed Full-Time

Master's Degree

Less than one month

Getting past phone screens

3-4 years

Green Card

0.0

9

Male

Black, Afro-Caribbean, or African American

5

8

Withdrawn (Failed)

SWE

AUG19A

19.0

0

Employed Part-Time

Bachelor's Degree

Less than one month

Getting past final round interviews

1-2 years

Citizen

4.0

15

Female

Latino or Hispanic American

6

10

Withdrawn (Trial)

SWE

SEP19A

13.0

0

Employed Full-Time

Master's Degree

Less than one month

Getting past final round interviews

3-4 years

Citizen

0.0

10

Male

Black, Afro-Caribbean, or African American

7

11

Closed Lost

PSO

AUG19B

0.0

0

Unemployed

Master's Degree

1-2 months

Hearing back on my applications

1-2 years

Other

0.0

3

Male

Latino or Hispanic American

8

12

Withdrawn

Data

AUG19C

158.0

0

Unemployed

Master's Degree

3-5 months

Lack of relevant experience

5+ years

Citizen

5.0

50

Male

Decline to Self Identify

9

13

Withdrawn (Trial)

Design

OCT19A

12.0

0

Contractor

Bachelor's Degree

6 months to a year

Getting past phone screens

1-2 years

Green Card

3.0

10

Male

Middle Eastern or Arab American

Report generated by YData.